Integration of Artificial Intelligence into Safety, Health & Environment (SHE) Management

Abstract

This thesis discusses the integration of artificial intelligence (AI) into Safety, Health and Environment (SHE) management through the practices, digital preparedness as well as the perceptions of four case companies in Finland and Pakistan. The investigation based on qualitative research design and semi-structured interviews determines the existing issues in hazard recognition, employee observation, documentation quality, and equipment maintenance. The results indicate that AI-enabled systems, such as computer vision systems, wearable health sensors, predictive maintenance platforms, and natural language processing have the potential of greatly improving proactive safety performance as long as they are aligned with organizational requirements. More specifically, computer vision has the potential to enhance real-time unsafe behavior detection, wearable can be used to enable continuous physiological measuring, and predictive analytics can be applied to detect equipment anomalies prior to failure. The findings also suggest that organizations that have more robust data governance and digital maturity are in good position to embrace AI in accordance with the ISO 31000 framework of systematic and data-based risk management. The research is relevant to the understanding of how AI can transform SHE practices into predictive instead of reactive and provide useful recommendations to companies that want to reinforce safety outcomes with the help of technological innovation.